AI Cybersecurity

AI Cybersecurity Strategy

AI systems introduce attack vectors that traditional security programs were never designed to handle — prompt injection, model manipulation, data poisoning, autonomous agent risk, and adversarial machine learning. Protecting AI isn't a governance exercise. It requires a full cybersecurity strategy: technical controls, threat defense, risk management, operational monitoring, and regulatory compliance — unified under one approach.

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The Threat Landscape

AI Is Creating Risks Your Security Program Wasn't Built For

Traditional cybersecurity controls don't account for AI-specific attack vectors — prompt injection, training data poisoning, model theft, adversarial evasion, and autonomous agent exploitation. Organizations need dedicated AI cybersecurity capabilities across their entire stack: from model-level defenses to enterprise-wide governance. The gap is technical, operational, and strategic — and it's growing every quarter.

3x
Attack Surface Growth
Every AI system introduces new attack vectors — prompt injection, model manipulation, data poisoning, supply chain compromise — that traditional controls can't detect or defend against
78%
Shadow AI Exposure
of organizations report employees using unapproved AI tools with corporate data — creating unmonitored data flows, unvetted models, and invisible exfiltration pathways
$4.8M
AI-Involved Breach Cost
Breaches involving AI systems carry higher costs due to data sensitivity, model complexity, regulatory exposure, and the difficulty of containing AI-specific attack chains
12%
Have AI Security Programs
Only a fraction of enterprises have dedicated AI cybersecurity capabilities — most are relying on traditional controls that weren't designed for AI-specific threats
Technical: Prompt injection, model evasion, and supply chain attacks exploit vulnerabilities that firewalls and endpoint detection were never designed to catch
Operational: Shadow AI, unmonitored model behavior, and autonomous agents making decisions without human oversight create blind spots across the organization
Strategic: Without AI-specific threat modeling and red teaming, organizations cannot quantify AI risk or prioritize investment — leaving leadership making decisions with incomplete information
Regulatory: The EU AI Act, state-level AI laws, and sector-specific requirements are creating compliance obligations that require dedicated cybersecurity capabilities — not just policy documents

Regulatory Pressure

Compliance Is One Dimension — But the Deadline Is Real

AI cybersecurity strategy extends far beyond regulatory compliance — but the EU AI Act creates a hard deadline that forces the conversation. Organizations operating high-risk AI systems must demonstrate compliance by August 2026 or face penalties of up to 7% of global annual revenue. The organizations that treat this as a catalyst for building real AI security capabilities — not just compliance artifacts — will be the ones that are actually protected.

August 2024
Act Enters Into Force
Complete
February 2025
Prohibited AI Practices Banned
Complete
August 2025
GPAI Rules Apply
Complete
August 2026
High-Risk AI Compliance Required
Upcoming days remaining
August 2027
Full Enforcement
Future

Full-Spectrum Coverage

Seven Frameworks. Every Layer of AI Security.

Each framework addresses a different dimension of AI cybersecurity — from adversarial threat modeling and technical vulnerability assessment to risk management, operational controls, and regulatory compliance. We integrate all seven into a single strategy that covers attacks, defenses, governance, and operations across your entire AI ecosystem.

Gartner CISO MCP 2026

Maturity model — measures your AI security capability from reactive to strategic across 5 stages

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Stage 1 — Reactive: No formal AI security program; ad-hoc responses to incidents
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Stage 2 — Aware: AI inventory initiated; basic risk identification underway
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Stage 3 — Proactive: Governance policies in place; systematic risk assessment across AI systems
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Stage 4 — Managed: Continuous monitoring; integrated AI risk into enterprise risk management
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Stage 5 — Strategic: AI security embedded in business strategy; board-level reporting and optimization

NIST AI RMF

Risk management — structured lifecycle for identifying, quantifying, and mitigating AI-specific threats

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Govern: Establish policies, roles, and accountability structures for AI risk
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Map: Identify and categorize AI systems, data flows, and context of use
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Measure: Assess and quantify risks using metrics, testing, and evaluation methods
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Manage: Prioritize, respond to, and monitor identified risks throughout the AI lifecycle

OWASP LLM Top 10 2025

Vulnerability assessment — the 10 most critical attack vectors targeting LLM-powered applications

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Prompt Injection: Manipulating LLM behavior through crafted inputs to bypass safety controls
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Sensitive Information Disclosure: Models leaking training data, PII, or proprietary information
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Supply Chain Vulnerabilities: Compromised models, datasets, or plugins introducing hidden risks
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Data & Model Poisoning: Corrupting training data to manipulate model outputs at scale
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+ 6 more vulnerabilities covering excessive agency, overreliance, insecure output handling, and model theft

OWASP Agentic Apps 2026

Agent security — attack surface analysis for autonomous AI systems making real-world decisions

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Excessive Autonomy: Agents making high-impact decisions without human approval checkpoints
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Trust Boundary Violations: Agents accessing systems or data beyond their intended scope
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Cascading Hallucinations: Agent errors compounding through multi-step workflows into real-world actions
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Tool Misuse: Agents invoking external tools or APIs in unintended or adversarial ways

MITRE ATLAS

Threat intelligence — 15 adversarial tactics and 66 techniques used to attack AI systems in the wild

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Reconnaissance: Adversaries probing AI systems to understand model architecture and training data
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Model Evasion: Crafting inputs that cause misclassification while appearing normal to humans
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Model Theft: Extracting model weights or behavior through repeated API queries
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ML Supply Chain: Compromising model registries, training pipelines, or deployment infrastructure
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+ 11 more tactics covering persistence, exfiltration, initial access, and impact across AI system lifecycles

EU AI Act

Regulatory compliance — risk classification, conformity assessment, and enforcement timeline for AI systems

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Unacceptable Risk: Banned AI practices — social scoring, real-time biometric surveillance, manipulation
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High Risk: Systems in healthcare, employment, finance, law enforcement — require conformity assessment
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Limited Risk: Chatbots and deepfakes — transparency obligations to inform users of AI interaction
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Minimal Risk: Most AI systems — no specific obligations, voluntary codes of conduct encouraged
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Penalties: Up to 7% of global annual turnover or 35M EUR for non-compliance

ISO 42001

Operational controls — certifiable management system for AI development, deployment, and monitoring

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AI Policy: Formal organizational commitment to responsible AI development and deployment
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Risk Treatment: Systematic identification, assessment, and mitigation of AI-specific risks
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Data Governance: Controls for data quality, provenance, bias detection, and privacy protection
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Continuous Improvement: Audit cycles, performance evaluation, and management review processes
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Certification: Third-party auditable standard demonstrating AI governance maturity to stakeholders

Engagement Model

From Visibility to Continuous Defense

Free

AI Security Discovery

15-question diagnostic that maps your AI security posture across all 7 frameworks. Identifies capability gaps in technical controls, risk management, and compliance — with immediate, actionable results.

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AI Security Assessment

50-question deep assessment covering AI threat exposure, vulnerability posture (OWASP LLM Top 10), adversarial resilience (MITRE ATLAS), risk management maturity, and regulatory readiness — with prioritized remediation roadmap.

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AI Security Operations

Continuous AI security monitoring, red team testing, framework score trending, compliance tracking, and quarterly strategic advisory — building sustained cybersecurity capability around your AI ecosystem.

Quarterly advisory
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See where your AI security stands

Our free 15-question diagnostic maps your AI cybersecurity posture across seven frameworks — covering technical controls, threat exposure, risk management, and compliance. Takes under seven minutes. No account required. Immediate results with prioritized next steps.

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